Context-Based Target Recognition

ABSTRACT

A method and apparatus for identifying a target object. A group of objects is identified in an image. The group of objects provides a context for identifying the target object in the image. The target object is searched for in the image using the context provided by the group of objects.

BACKGROUND INFORMATION

1. Field

The present disclosure relates generally to processing images and, inparticular, to identifying objects in images. Still more particularly,the present disclosure relates to a method and apparatus for identifyingtarget objects in images using contextual information in the images.

2. Background

Target recognition is the identification of one or more target objectsin a number of images. The target objects to be identified may include,for example, without limitation, people, vehicles, buildings,structures, geographic features, and/or other types of objects. Targetrecognition may be used when performing various types of missions. Thesemissions may include, for example, without limitation, surveillance,reconnaissance, weapons deployment, cargo drops, and/or other suitabletypes of missions.

Oftentimes, radar imaging is used to generate the images used in thesetypes of missions. Synthetic aperture radar (SAR) imaging is a form ofradar imaging in which an antenna may be mounted to a mobile platform.Typically, the mobile platform is an aerospace platform. An aerospaceplatform may be, for example, without limitation, an aircraft, anunmanned aerial vehicle, a helicopter, a missile, a satellite, a spaceshuttle, or some other suitable type of aerospace platform.

With synthetic aperture radar imaging, the mobile platform carrying asynthetic aperture radar system moves along a path. As the mobileplatform moves along the path, an antenna in the synthetic apertureradar system sends pulses of electromagnetic radiation. Thiselectromagnetic radiation is in the form of electromagnetic waves, whichare also referred to as electromagnetic signals. These electromagneticsignals may have wavelengths, for example, from about 10 millimeters toabout one meter.

The electromagnetic signals are directed at an area. The area may be,for example, an area of terrain, a block in a neighborhood, a section ofa forest, a portion of a city, a plant, a bridge, or some other suitabletype of area.

When these electromagnetic signals encounter a surface of the area, atleast a portion of the electromagnetic signals is reflected off of thesurface. The electromagnetic waves that are reflected off the surfacemay be referred to as backscatter, scattered electromagnetic waves,scattered electromagnetic signals, echo waves, or echoes.

As the mobile platform moves along the path over the area, the antennadetects the electromagnetic signals reflected off of the surface inresponse to the pulses sent by the antenna. The electromagnetic signalsreceived at the antenna as the antenna moves along the path areprocessed to form an image of the area.

Additionally, synthetic aperture radar imaging may be implemented asinverse synthetic aperture radar imaging. This type of imaging isperformed using a stationary antenna or an antenna mounted to astationary platform. An image is generated for moving objects in thearea observed over time.

With currently-available systems for target recognition using the numberof images generated using synthetic aperture radar imaging, falseidentifications of objects may occur more often than desired. Correctiveactions may need to be taken when false identifications occur. As aresult, additional processing may occur to verify that the objects thathave been identified are the target objects. Further, the time needed torecognize objects may be important when using that information tocontrol the mobile platform or perform other actions that are timesensitive. The time needed to perform target recognition usingcurrently-available systems may be greater than desired.

Therefore, it would be advantageous to have a method and apparatus thattakes into account at least some of the issues discussed above, as wellas possibly other issues.

SUMMARY

In one advantageous embodiment, a method is provided for identifying atarget object. A group of objects is identified in an image. The groupof objects provides a context for identifying the target object in theimage. The target object is searched for in the image using the contextprovided by the group of objects.

In another advantageous embodiment, an apparatus comprises a contextrecognition module and a target recognition module. The contextrecognition module is configured to identify a group of objects in animage. The group of objects provides a context for identifying a targetobject in the image. The target recognition module is configured tosearch for the target object in the image using the context provided bythe group of objects.

The features, functions, and advantages can be achieved independently invarious embodiments of the present disclosure or may be combined in yetother embodiments in which further details can be seen with reference tothe following description and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features believed characteristic of the advantageousembodiments are set forth in the appended claims. The advantageousembodiments, however, as well as a preferred mode of use, furtherobjectives, and advantages thereof, will best be understood by referenceto the following detailed description of an advantageous embodiment ofthe present disclosure when read in conjunction with the accompanyingdrawings, wherein:

FIG. 1 is an illustration of an imaging environment in accordance withan advantageous embodiment;

FIG. 2 is an illustration of an imaging environment in accordance withan advantageous embodiment;

FIG. 3 is an illustration of a building detector in accordance with anadvantageous embodiment;

FIG. 4 is an illustration of a detection of lines in an image inaccordance with an advantageous embodiment;

FIG. 5 is an illustration of a cumulative histogram in accordance withan advantageous embodiment;

FIG. 6 is an illustration of a method for identifying bright areas inaccordance with an advantageous embodiment;

FIG. 7 is an illustration of a binary image in which bright areas arepresent in accordance with an advantageous embodiment;

FIG. 8 is another illustration of a binary image in which bright areasare present in accordance with an advantageous embodiment;

FIG. 9 is another illustration of a binary image in which bright areasare present in accordance with an advantageous embodiment;

FIG. 10 is another illustration of a binary image in which bright areasare present in accordance with an advantageous embodiment;

FIG. 11 is an illustration of an image generated using a syntheticaperture radar imaging system in accordance with an advantageousembodiment;

FIG. 12 is an illustration of bright areas and shadows identified in animage in accordance with an advantageous embodiment;

FIG. 13 is an illustration of a building mask for an image in accordancewith an advantageous embodiment;

FIG. 14 is an illustration of a vegetation detector in accordance withan advantageous embodiment;

FIG. 15 is an illustration of a process for identifying vegetation inaccordance with an advantageous embodiment;

FIG. 16 is an illustration of a vegetation mask for an image inaccordance with an advantageous embodiment;

FIG. 17 is an illustration of a binary image for use in identifyingvegetation in accordance with an advantageous embodiment;

FIG. 18 is an illustration of a road detector in accordance with anadvantageous embodiment;

FIG. 19 is an illustration of an image generated using a syntheticaperture radar imaging system in accordance with an advantageousembodiment;

FIG. 20 is an illustration of a processed image for use in identifyingroads in accordance with an advantageous embodiment;

FIG. 21 is an illustration of a road mask for an image in accordancewith an advantageous embodiment;

FIG. 22 is an illustration of a flowchart of a process for identifying atarget object in accordance with an advantageous embodiment;

FIG. 23 is an illustration of a flowchart of a process for identifying atarget object in an image in accordance with an advantageous embodiment;

FIG. 24 is an illustration of a flowchart of a process for detectingbuildings in an image in accordance with an advantageous embodiment;

FIG. 25 is an illustration of a flowchart of a process for detectingvegetation in an image in accordance with an advantageous embodiment;

FIG. 26 is an illustration of a flowchart of a process for detectingroads in an image in accordance with an advantageous embodiment; and

FIG. 27 is an illustration of a data processing system in accordancewith an advantageous embodiment.

DETAILED DESCRIPTION

Referring now to the figures, in FIG. 1, an illustration of an imagingenvironment is depicted in accordance with an advantageous embodiment.In this illustrative example, imaging environment 100 is an environmentin which images may be generated for area 102. Area 102 includes terrain104, vegetation 106, buildings 108, road 110, ground station 111, andother objects within area 102.

As depicted, unmanned aerial vehicle (UAV) 112 is present in imagingenvironment 100. Radar imaging system 114 is mounted to unmanned aerialvehicle 112. Radar imaging system 114 is a synthetic aperture radar(SAR) imaging system in this illustrative example.

Radar imaging system 114 is configured to send pulses of electromagneticsignals towards area 102 as unmanned aerial vehicle 112 moves alongflight path 116 in the direction of arrow 118. Further, radar imagingsystem 114 is configured to detect electromagnetic signals reflected offof the various surfaces in area 102. These surfaces may include thesurfaces of, for example, terrain 104, vegetation 106, buildings 108,and/or road 110.

Radar imaging system 114 processes the electromagnetic signals receivedas unmanned aerial vehicle 112 flies over area 102 along flight path 116to generate an image of area 102. This image may be used to identify anumber of target objects in area 102. As used herein, “a number ofitems” means one or more items. For example, “a number of targetobjects” means one or more target objects.

A target object may take the form of, for example, without limitation,at least one of a person, a vehicle, a tank, a truck, a motorcycle, acar, a missile, a mobile object, and/or some other suitable type ofobject. As used herein, the phrase “at least one of”, when used with alist of items, means that different combinations of one or more of thelisted items may be used, and only one of each item in the list may beneeded.

For example, “at least one of item A, item B, and item C” may include,for example, without limitation, item A, or item A and item B. Thisexample also may include item A, item B, and item C, or item B and itemC. In other examples, “at least one of” may be, for example, withoutlimitation, two of item A, one of item B, and 10 of item C; four of itemB and seven of item C; and other suitable combinations.

In this illustrative example, ground station 111 and/or unmanned aerialvehicle 112 may be configured to process images generated by radarimaging system 114 in accordance with an advantageous embodiment. Inparticular, ground station 111 and/or unmanned aerial vehicle 112 mayprocess the images of area 102 to identify target objects in the imagesin accordance with an advantageous embodiment. This processing may beperformed to reduce the number of false identifications of targetobjects in the images.

The different advantageous embodiments recognize and take into account anumber of different considerations. For example, the differentadvantageous embodiments recognize and take into account that somecurrently-available methods for identifying a target object in an imagesearch for the target object in a scene in the image using a pre-definedmodel for the target object. The different advantageous embodimentsrecognize and take into account that these methods may not usecontextual information present in the scene in the image whenidentifying the target object.

Contextual information in an image may include, for example, anidentification of buildings, vegetation, roads, and/or other structuresin the scene in the image and/or other suitable information identifiedusing the image. The different advantageous embodiments recognize andtake into account that identifying the target object using contextualinformation identified in the image may reduce an amount of space in thescene in the image that needs to be searched as compared to usingcurrently-available methods for identifying target objects.

Further, the different advantageous embodiments recognize and take intoaccount that currently-available systems for identifying target objectsmay take more time and/or effort than desired. However, the differentadvantageous embodiments recognize and take into account that usingcontextual information in the image to identify the target object mayallow target objects to be identified more quickly than withcurrently-available methods.

Additionally, the different advantageous embodiments recognize and takeinto account that noise and/or other undesired features may be presentin an image. These undesired features may lead to a greater number offalse identifications of target objects than desired. The differentadvantageous embodiments recognize and take into account that usingcontextual information in the image to identify the target object mayreduce the number of false identifications of target objects as comparedto currently available methods.

Thus, the different advantageous embodiments provide a method andapparatus for identifying a target object in an image. In particular, amethod and apparatus for identifying a target object in an image usingcontextual information identified in the image is provided. In oneadvantageous embodiment, a group of objects is identified in an image.The group of objects provides a context for identifying a target objectin the image. The image is then searched for the target object using thecontext provided by the group of objects.

With reference now to FIG. 2, an illustration of an imaging environmentis depicted in accordance with an advantageous embodiment. Imagingenvironment 100 in FIG. 1 is an example of one implementation forimaging environment 200. As depicted, imaging environment 200 includesplatform 204, imaging system 206, area 208, and image processing module210.

Platform 204 may be selected from one of, for example, withoutlimitation, a mobile platform, a stationary platform, an aerialplatform, a land-based platform, an aquatic-based platform, aspace-based platform, and/or some other suitable type of platform. Forexample, platform 204 may take the form of an aircraft, an unmannedaerial vehicle, a missile, a helicopter, a spacecraft, a satellite, aspace station, a submarine, a bus, a personnel carrier, a tank, a train,an automobile, a surface ship, a building, and/or some other suitableobject. In these illustrative examples, platform 204 takes the form ofmobile platform 211.

In these depicted examples, imaging system 206 is associated with mobileplatform 211. A first component, such as imaging system 206, may beconsidered to be associated with a second component, such as mobileplatform 211, by being secured to the second component, bonded to thesecond component, welded to the second component, fastened to the secondcomponent, mounted to the second component, and/or connected to thesecond component in some other suitable manner. The first component alsomay be connected to the second component using a third component. Thefirst component may also be considered to be associated with the secondcomponent by being formed as part of and/or an extension of the secondcomponent.

As depicted, imaging system 206 is radar imaging system 209. Morespecifically, radar imaging system 209 takes the form of syntheticaperture radar (SAR) imaging system 212 in these illustrative examples.Imaging system 206 may comprise antenna 214 and signal processing system216.

In these illustrative examples, antenna 214 is configured to send pulses218 of first electromagnetic signals 220 towards area 208 in imagingenvironment 200 as mobile platform 211 moves along path 221 relative toarea 208. Path 221 may be over area 208, to a side of area 208, behindarea 208, and/or relative to area 208 in some other manner.

Area 208 may include, for example, without limitation, at least one of aregion of terrain, a city, a block in a neighborhood, a town, an area ofa city, a forest area, a mountainous region, a valley, an area of anocean, a lake, a manufacturing facility, a power plant, a region ofairspace, a region of space, and some other suitable type of area.Objects 222 may be present in area 208. In these illustrative examples,objects 222 may include at least one of a building, a tree, a plant, abush, a road, a highway, a structure, a sidewalk, a parking lot, aparking garage, a person, a feature of terrain in area 208, and othersuitable types of objects.

At least a portion of first electromagnetic signals 220 sent by antenna214 are reflected off of surface 224 of area 208. Surface 224 of area208 includes the surface of any terrain in area 208 and the surfaces ofobjects 222 in area 208.

In these depicted examples, the portion of first electromagnetic signals220 reflected off of surface 224 of area 208 forms secondelectromagnetic signals 226. Second electromagnetic signals 226 also maybe referred to as backscatter, scattered electromagnetic signals, orechoes.

Antenna 214 is configured to detect second electromagnetic signals 226over time as mobile platform 211 moves along path 221 relative to area208. Antenna 214 sends second electromagnetic signals 226 received atantenna 214 to signal processing system 216 for processing. Signalprocessing system 216 may be implemented using hardware, software, or acombination of the two in these examples.

As depicted, signal processing system 216 is configured to generateimage 228 of area 208 using second electromagnetic signals 226. In someillustrative examples, signal processing system 216 may be configured toperform preprocessing operations prior to generating image 228. In otherillustrative examples, signal processing system 216 may includeinformation with image 228. This information may include, for example,without limitation, a timestamp, a location of mobile platform 211,and/or other suitable information.

In these illustrative examples, signal processing system 216 sends image228 to image processing module 210. Image processing module 210 may beimplemented using hardware, software, or a combination of the two. Inthese illustrative examples, image processing module 210 may beimplemented in computer system 230. Computer system 230 may take theform of number of computers 231.

Image processing module 210 may be located remote to imaging system 206in these illustrative examples. For example, image processing module 210may be located in mobile platform 211. In other illustrative examples,image processing module 210 may be located at a ground station, controltower, or some other location remote to signal processing system 216. Asone illustrative example, when image processing module 210 is located ata ground station, signal processing system 216 sends image 228 to imageprocessing module 210 using wireless communications link 232.

In these illustrative examples, image processing module 210 comprisescontext recognition module 234 and target recognition module 235. Inthese depicted examples, context recognition module 234 is configured toidentify group of objects 236 in scene 237 in image 228. As used herein,“a group of items” means one or more items. For example, “a group ofobjects” means one or more objects. Scene 237 is the portion of area 208captured in image 228.

Group of objects 236 provides context 238 for identifying target object240 in image 228. An object in group of objects 236 may be, for example,a building, vegetation, a road, or some other suitable type of object.Target object 240 may be any object that is an object of interest.

Context 238 for identifying target object 240 comprises the informationpresent in scene 237 in image 228 that may be used to identify targetobject 240 in image 228. Further, context 238 provided by group ofobjects 236 may be used to verify that an identification of an object inscene 237 is target object 240. Number of regions 242 in image 228 forsearching for target object 240 may be identified using context 238provided by group of objects 236.

In these illustrative examples, a region in number of regions 242 may beselected from within group of objects 236, within a selected distancefrom group of objects 236, outside of a selected distance from group ofobjects 236, and/or relative to group of objects 236 in some othersuitable manner.

In one illustrative example, context recognition module 234 forms numberof masks 244 using context 238 provided by group of objects 236. Contextrecognition module 234 uses number of masks 244 to identify number ofregions 242 in image 228 for searching for target object 240. Targetrecognition module 235 is configured to search for target object 240 innumber of regions 242 in image 228 using number of masks 244.

Further, target recognition module 235 is configured to search fortarget object 240 in number of regions 242 using number of agents 246.Agent 248 is an example of one of number of agents 246. Agent 248 is asearch process that is configured to search for target object 240 withinwindow 252 for agent 248. A size and/or shape for window 252 may beselected based on the type of target object 240 for which the search isperformed. Further, windows for different agents in number of agents 246may have the same or different sizes and/or shapes.

Number of agents 246 may function as a swarm and search for targetobject 240 using particle-swarm optimization (PSO) process 250. Thisprocess optimizes a problem by iteratively trying to improve a candidatesolution with regard to a given measure of quality. For example, aplacement of window 252 for agent 248 relative to image 228 may be acandidate solution for the identification of target object 240. Thecandidate solution also may be referred to as a particle.

Window 252 is moved within number of regions 242 in image 228 accordingto simple mathematical formulae for a position and velocity for window252. In these illustrative examples, agent 248 identifies a best knownposition for window 252 in number of regions 242. Further, other agentsin number of agents 246 identify best known positions for windowscorresponding to these other agents. These best known positions areupdated as better positions are identified.

The position and/or movement of window 252 within number of regions 242in image 228 is guided by the best known position identified by agent248 and the updated best known positions for the other agents in numberof agents 246. In this manner, number of agents 246 may move as a swarmtowards the best position in number of regions 242 for identifyingtarget object 240.

In some illustrative examples, context 238 for identifying target object240 may also be provided by additional information 254. Additionalinformation 254 may include information obtained from sources other thanimage 228. Additional information 254 may include, for example, withoutlimitation, intelligence information, geographical data, maps, missiondetails, weather data, location information, and/or other suitable typesof information.

In this manner, target recognition module 235 uses context 238 providedby group of objects 236 to identify target object 240 in image 228 morequickly as compared to currently-available methods that may search allof image 228 for target object 240.

Further, using target recognition module 235 may reduce the possibilityof a false identification of target object 240 in image 228. Forexample, target object 240 to be identified in image 228 may be a tank.In this example, group of objects 236 provide context 238 foridentifying the tank. Group of objects 236 may comprise, for example,vegetation, such as trees. Number of regions 242 is identified usingcontext 238 provided by the trees such that target recognition module235 does not identify the tank within the trees.

The illustration of imaging environment 200 in FIG. 2 is not meant toimply physical or architectural limitations to the manner in which anadvantageous embodiment may be implemented. Other components in additionto and/or in place of the ones illustrated may be used. Some componentsmay be unnecessary. Also, the blocks are presented to illustrate somefunctional components. One or more of these blocks may be combinedand/or divided into different blocks when implemented in an advantageousembodiment.

For example, in some illustrative examples, platform 204 may be astationary platform instead of mobile platform 211. In otherillustrative examples, a portion of image processing module 210 may bepart of signal processing system 216 in imaging system 206. A portion ofimage processing module 210 may be some or all of image processingmodule 210 in these illustrative examples.

Further, in other illustrative examples, target recognition module 235may be configured to identify other target objects in addition to and/orin place of target object 240. In still other illustrative examples,target recognition module 235 may use other processes, in addition toand/or in place of particle-swarm optimization process 250 to search fortarget object 240 in image 228.

With reference now to FIG. 3, an illustration of a building detector isdepicted in accordance with an advantageous embodiment. Buildingdetector 300 may be implemented in context recognition module 234 inFIG. 2. In particular, building detector 300 is configured to identifybuildings that may be in image 228 in FIG. 2. Any buildings that areidentified may form a portion of group of objects 236 in FIG. 2.

In this illustrative example, building detector 300 comprises noisefilter 302, line detector 304, area detector 306, shadow detector 308,and classification module 310. Noise filter 302 is configured to reducenoise 312 that may be present in image 228. The reduction of noise 312may substantially remove noise in image 228 in a manner that provides adesired quality for image 228.

Noise 312 may take the form of, for example, speckle noise 314. Specklenoise 314 may be a granular noise that is present in image 228. Specklenoise 314 may be caused by undesired random fluctuations in secondelectromagnetic signals 226 received at antenna 214 in imaging system206 in FIG. 2.

As depicted, after noise filter 302 processes image 228 to reduce noise312, processed image 316 is present. Line detector 304 is configured toidentify set of lines 318 in processed image 316. As used herein, “a setof items” may be zero or more items. For example, “a set” may be anempty or null set. In this depicted example, lines in set of lines 318are bright lines.

In this illustrative example, line detector 304 may be implemented usingan algorithm developed by Carsten Steger. This algorithm extracts set oflines 318 from processed image 316 and estimates a width for each of setof lines 318. This algorithm also identifies a position for each line inset of lines 318 and estimates the width for each line with a sub-pixellevel of accuracy.

Further, area detector 306 is configured to identify set of bright areas324 in this illustrative example. Set of bright areas 324 is identifiedusing number of thresholds 326 for intensity 327 in processed image 316.Intensity 327 for each pixel in processed image 316 may be from aboutzero to about 255. In particular, a portion of set of bright areas 324may be identified for each threshold in number of thresholds 326 appliedto processed image 316.

In this illustrative example, number of thresholds 326 is selected basedon cumulative histogram 329 for processed image 316. Cumulativehistogram 329 plots level of intensity versus a cumulative percentage ofpixels in processed image 316.

In particular, first threshold 328 in number of thresholds 326 may beselected based on percentage 330. Percentage 330 is selected usingcumulative histogram 329. Percentage 330 is the percentage of pixelsthat are to be included in the process for identifying set of brightareas 324. Percentage 330 is selected to be between about 85 percent andabout 95 percent. As one illustrative example, first threshold 328 maybe selected as the level of intensity at which percentage 330 is about90 percent.

In this illustrative example, other thresholds in number of thresholds326 may be selected by substantially equally dividing the intervalbetween first threshold 328 and a highest level of intensity 327 inprocessed image 316 by a total number of thresholds in number ofthresholds 326.

Area detector 306 applies number of thresholds 326 to processed image316 to identify number of binary images 331. A binary image is an imagein which each pixel in the binary image has only two values. These twovalues are first value 332 and second value 334.

In this illustrative example, first value 332 for the pixel indicatesthat the intensity for that pixel is less than the particular thresholdfrom number of thresholds 326 applied to the pixel. Second value 334 forthe pixel indicates that the intensity for that pixel is greater thanthe particular threshold from number of thresholds 326 applied to thepixel. Pixels in a binary image in number of binary images 331 that havesecond value 334 are identified as being part of a bright area.

Set of bright areas 324 may be present in number of binary images 331. Abinary image in number of binary images 331 may include bright areasthat are not present in other binary images in number of binary images331. In some cases, no bright areas may be present in one or more ofnumber of binary images 331.

Of course, other processes for identifying set of bright areas 324 maybe used. For example, in some cases, area detector 306 may be configuredto identify set of bright areas 324 using image 228 instead of processedimage 316.

In this illustrative example, shadow detector 308 is configured toidentify set of shadows 336. Shadow detector 308 identifies set ofshadows 336 in a manner similar to the manner in which area detector 306identifies set of bright areas 324. For example, shadow detector 308applies number of thresholds 326 to processed image 316. First threshold328 in number of thresholds 326 is selected based on percentage 330.Percentage 330 may be selected to be between about 20 percent and about25 percent.

Other thresholds in number of thresholds 326 are selected bysubstantially equally dividing the interval between first threshold 328and a lowest level of intensity 327 in processed image 316 by the totalnumber of thresholds in number of thresholds 326. Number of thresholds326 may be applied to processed image 316 to obtain number of binaryimages 331. Set of shadows 336 is present in number of binary images331.

In this illustrative example, classification module 310 is configured toprocess set of lines 318, set of bright areas 324, and set of shadows336 to identify which of the items in these sets are produced bybuildings in image 228. Classification module 310 identifies set ofbuildings 350 using set of lines 318, set of bright areas 324, set ofshadows 336, and/or other suitable information.

Further, classification module 310 may generate building mask 352 byidentifying set of buildings 350 in image 228. Building mask 352 mayinclude, for example, locations of set of buildings 350 in image 228.

The illustration of building detector 300 in FIG. 3 is not meant toimply limitations to the manner in which an advantageous embodiment maybe implemented. For example, in some illustrative examples, thresholdsin number of thresholds 326 other than first threshold 328 may beselected by identifying the centroids of clusters of pixels identifiedbased on intensity. The clusters may be identified using, for example, aK-means clustering algorithm and/or other suitable clusteringalgorithms.

With reference now to FIG. 4, an illustration of a detection of lines inan image is depicted in accordance with an advantageous embodiment. Inthis illustrative example, lines 400 have been identified in image 402.The detection of lines 400 may have been made by, for example, linedetector 304 in building detector 300 in FIG. 3.

Lines 400 may be filtered to identify a set of lines, such as set oflines 318 in FIG. 3. Lines 400 may be filtered using a number ofcriteria. In particular, any line in lines 400 having a length in image402 that corresponds to a length of at least about 10 meters in the realworld is selected. The Hough transform may be applied to these selectedlines to further filter lines 400 to identify the set of lines. Usingthe Hough transform, any line having a substantially straight portionwith a length corresponding to a length of at least about 10 meters inthe real world is included in the set of lines.

With reference now to FIG. 5, an illustration of a cumulative histogramis depicted in accordance with an advantageous embodiment. In thisillustrative example, cumulative histogram 500 is an example of oneimplementation for cumulative histogram 329 in FIG. 3.

As depicted, cumulative histogram 500 has horizontal axis 502 andvertical axis 504. Horizontal axis 502 is intensity 506. Vertical axis504 is percentage of pixels 508.

In this illustrative example, first threshold 510, T_(o), in number ofthresholds 512 is selected based on percentage 514, P_(o). Otherthresholds in number of thresholds 512 are selected by dividing theinterval along horizontal axis 502 between first threshold 510 andhighest intensity 516, N, by the total number of thresholds in number ofthresholds 512.

With reference now to FIG. 6, an illustration of a method foridentifying bright areas is depicted in accordance with an advantageousembodiment. In this illustrative example, intensity 600 is the intensityfor pixels in section 601 of an image, such as processed image 316 inFIG. 3.

First level of intensity 602 is a first threshold in a number ofthresholds to be applied to the image. Second level of intensity 604represents a second threshold in the number of thresholds. Third levelof intensity 606 represents a third threshold in the number ofthresholds. Fourth level of intensity 608 represents a fourth thresholdin the number of thresholds.

As depicted in this example, first portion 610 of section 601 is theportion of section 601 having pixels with an intensity substantiallyequal to or greater than first level of intensity 602. Second portion612 of section 601 is the portion of section 601 having pixels with anintensity substantially equal to or greater than second level ofintensity 604.

Third portion 614 of section 601 is the portion of section 601 havingpixels with an intensity substantially equal to or greater than thirdlevel of intensity 606. Fourth portion 616 of section 601 is the portionof section 601 having pixels with an intensity substantially equal to orgreater than fourth level of intensity 608.

With reference now to FIGS. 7-10, illustrations of binary images inwhich bright areas present are depicted in accordance with anadvantageous embodiment. In these illustrative examples, these binaryimages may be generated by an area detector, such as area detector 306in building detector 300 in FIG. 3. In particular, these binary imagesmay be generated by filtering a processed image, such as processed image316 in FIG. 3, for varying levels of intensity.

Turning now to FIG. 7, binary image 700 is generated by filtering theprocessed image for pixels having an intensity substantially equal to orgreater than first level of intensity 602 in FIG. 6. The portions of theprocessed image having pixels with an intensity substantially equal toor greater than first level of intensity 602 are represented in binaryimage 700 as bright areas 702.

Turning now to FIG. 8, binary image 800 is generated by filtering theprocessed image for pixels having an intensity substantially equal to orgreater than second level of intensity 604 in FIG. 6. The portions ofthe processed image having pixels with an intensity substantially equalto or greater than second level of intensity 604 are represented inbinary image 800 as bright areas 802.

Similarly, in FIG. 9, binary image 900 is generated by filtering theprocessed image for pixels having an intensity substantially equal to orgreater than third level of intensity 606 in FIG. 6. The portions of theprocessed image having pixels with an intensity substantially equal toor greater than third level of intensity 606 are represented in binaryimage 900 as bright areas 902.

Further, in FIG. 10, binary image 1000 is generated by filtering theprocessed image for pixels having an intensity substantially equal to orgreater than fourth level of intensity 608 in FIG. 6. The portions ofthe processed image having pixels with an intensity substantially equalto or greater than fourth level of intensity 608 are represented inbinary image 1000 as bright areas 1002.

In these illustrative examples, bright areas 702 in FIG. 7, bright areas802 in FIG. 8, bright areas 902 in FIG. 9, and bright areas 1002 in FIG.10 are processed to identify bright areas that represent buildings. Inparticular, a confidence score is assigned to each bright area.

The confidence score may be identified based on shape descriptors, suchas, for example, elliptical Fourier descriptors, for the bright areas.These types of shape descriptors may be used given that buildingstypically produce a rectangular or L-shaped bright area. The confidencescore may be identified based on how closely the shape for a bright areamatches a shape descriptor for the bright area. Further, the confidencescore may be identified based on training tests for typical bright areasproduced by buildings.

In one illustrative example, the confidence scores for a bright area ineach of the different levels of intensity may be summed to identify aconfidence score for the bright area. This confidence score may then becompared to a selected threshold. A bright area having a confidencescore greater than the selected threshold may be identified as a brightarea that may have been produced by a building.

In these illustrative examples, shadows that may have been produced bybuildings are identified in a manner similar to the way in which brightareas that may have been produced by buildings are identified.

With reference now to FIG. 11, an illustration of an image generatedusing a synthetic aperture radar imaging system is depicted inaccordance with an advantageous embodiment. In this illustrativeexample, image 1100 is an example of one implementation for image 228generated using synthetic aperture radar imaging system 212 in FIG. 2.As depicted, buildings 1102 are present in image 1100.

Turning now to FIG. 12, an illustration of bright areas and shadowsidentified in image 1100 is depicted in accordance with an advantageousembodiment. In this illustrative example, bright areas 1200 and shadows1202 are identified in image 1100.

Bright areas 1200 may be identified by, for example, area detector 306in building detector 300 in FIG. 3. Shadows 1202 may be identified by,for example, shadow detector 308 in building detector 300 in FIG. 3. Inthis illustrative example, the presence of both bright areas 1200 andshadows 1202 are used to identify buildings 1204 in image 1100.

Further, as depicted, heights 1206 may be identified for buildings 1204identified in image 1100. Heights 1206 are estimations of the actualheights for these buildings. In this illustrative example, heights 1206may be calculated by building detector 300 using the heights of shadows1202.

With reference now to FIG. 13, an illustration of a building mask forimage 1100 is depicted in accordance with an advantageous embodiment. Inthis illustrative example, building mask 1300 is overlaid on image 1100.Building mask 1300 is an example of one implementation for building mask352 in FIG. 3.

As depicted, building mask 1300 identifies buildings 1302 in locations1304 in image 1100. Buildings 1302 are identified based on theconfidence scores for lines, bright areas, and/or shadows identified forimage 1100. In particular, a building in buildings 1302 may berepresented by the identification of a line, a bright area, a shadow, orsome combination of these features.

Building mask 1300 may be used when identifying a target object in image1100. In other words, portions of image 1100 not identified as buildings1302 may be searched for the target object.

With reference now to FIG. 14, an illustration of a vegetation detectoris depicted in accordance with an advantageous embodiment. In thisillustrative example, vegetation detector 1400 may be implemented incontext recognition module 234 in FIG. 2. In particular, vegetationdetector 1400 is configured to identify vegetation that may be presentin image 228 in FIG. 2. Vegetation that is identified may form a portionof group of objects 236 in FIG. 2.

In this illustrative example, vegetation detector 1400 processes image228 from FIG. 2 using texture features filter 1402, number of textonlayout filters 1404, and boosted classifiers 1406. Boosted classifiers1406 may be a number of boosted classifiers in some illustrativeexamples. Vegetation detector 1400 applies texture features filter 1402to image 228. Texture features filter 1402 allows the portions of image228 that substantially match predefined texture features to beextracted.

In this depicted example, a texture feature is a feature in image 228,such as a pattern, that may represent a particular texture forvegetation and/or background in image 228. A texture feature may also bereferred to as a texton. A particular texture may be, for example,without limitation, grass, foliage, human skin, a road, sky, clouds,and/or some other suitable type of texture.

Vegetation detector 1400 identifies texture features 1408 in image 228in response to applying texture features filter 1402 to image 228.Vegetation detector 1400 uses texture features 1408 to generate textonmap 1410. Texton map 1410 identifies where texture features 1408 arelocated in image 228.

For example, vegetation detector 1400 may group texture features 1408together based on type using texton codebook 1411. This grouping mayalso be referred to as clustering and assignment. Texton codebook 1411is an identification of the types of texture features that might beidentified in the image.

Each group of texture features 1408 of a particular type is the outputof applying texture features filters 1402 to image 228 and correspondsto a shape in number of shapes 1412 on texton map 1410. Of course,multiple groups of the same type of texture features may be present.

In this illustrative example, number of shapes 1412 may be a number ofrectangular shapes. This number of rectangular shapes may have varyinglengths and/or widths. Of course, in other illustrative examples, numberof shapes 1412 may take the form of other shapes, such as, for example,circular shapes, elliptical shapes, and/or other suitable types ofshapes.

Further, in this depicted example, number of shapes 1412 may have numberof colors 1414. Each of number of colors 1414 corresponds to aparticular type of texture feature. As one illustrative example, whennumber of shapes 1412 represents texture features of two differenttypes, a portion of number of shapes 1412 will have a first color, whilea second portion of number of shapes 1412 has a second color. In somecases, all of number of shapes 1412 may have the same color when all ofnumber of shapes 1412 represents texture features of a same type.

As depicted in this example, vegetation detector 1400 uses texton map1410 to form number of texton channels 1416. Each texton channel innumber of texton channels 1416 is a map for texture features of aparticular type. In other words, each texton channel includes the shapesin number of shapes 1412 representing a particular type of texturefeature. In this manner, all of the shapes from number of shapes 1412 ina particular texton channel have the same color.

In this illustrative example, vegetation detector 1400 applies number oftexton layout filters 1404 to image 228 using number of texton channels1416. In particular, each of number of texton layout filters 1404corresponds to one of number of texton channels 1416. In other words,each of number of texton layout filters 1404 is for a particular type oftexture feature.

Texton layout filter 1418 is an example of one of number of textonlayout filters 1404. Texton layout filter 1418 is a combination of aregion in number of regions 1420 and a corresponding pixel marker innumber of pixel markers 1422. The region has a fixed size and positionwith respect to the corresponding pixel marker. Texton layout filter1418 compares each pixel in image 228 where a pixel marker is placedwith image information inside the corresponding region in number ofregions 1420.

Vegetation detector 1400 generates number of feature vectors 1424 inresponse to applying texton layout filter 1418. A feature vector innumber of feature vectors 1424 is an n-dimensional vector of numericalfeatures that represent some object or texture in image 228.

Number of feature vectors 1424 is processed using boosted classifiers1406 to identify texture mask 1426. Boosted classifiers 1406 are textureclassifiers that have been identified using boosting algorithms, suchas, for example, without limitation, adaptive boosting (AdaBoost),linear programming boosting (LPBoost), and/or other suitable types ofboosting algorithms.

Texture mask 1426 identifies textures 1428 in image 228. Textures 1428may be color-coded in this illustrative example. For example, grass maybe represented in texture mask 1426 by a first color, while sky isrepresented by a second color, and trees are presented by a third color.When texture mask 1426 is generated for only vegetation textures,texture mask 1426 may be a vegetation mask.

With reference now to FIG. 15, an illustration of an image generatedusing a synthetic aperture radar imaging system is depicted inaccordance with an advantageous embodiment. In this illustrativeexample, image 1500 is an example of one implementation for image 228generated using synthetic aperture radar imaging system 212 in FIG. 2.As depicted, vegetation 1502 is present in image 1500.

Turning now to FIG. 16, an illustration of a confidence mask for use inidentifying vegetation is depicted in accordance with an advantageousembodiment. In this illustrative example, confidence mask 1600 may begenerated by a vegetation detector, such as, for example, vegetationdetector 1400 in FIG. 14.

Confidence mask 1600 is an image in which the level of intensity foreach pixel is based on a confidence score for a corresponding pixel inimage 1500 in FIG. 15. This confidence score identifies the confidencewith which the pixel in image 1500 represents vegetation. A higher levelof intensity corresponds to a higher level of confidence that thecorresponding pixel is for vegetation as compared to a lower level ofintensity.

A vegetation mask may be identified by applying a threshold toconfidence mask 1600. In other words, pixels in confidence mask 1600having intensities greater than a selected threshold may be identifiedas vegetation.

With reference now to FIG. 17, an illustration of a vegetation mask foran image is depicted in accordance with an advantageous embodiment. Inthis illustrative example, vegetation mask 1700 is overlaid on image1500. Vegetation mask 1700 may be generated by a vegetation detector,such as, for example, vegetation detector 1400 in FIG. 14. Vegetationmask 1700 is identified by applying a threshold to confidence mask 1600in FIG. 16.

With reference now to FIG. 18, an illustration of a road detector isdepicted in accordance with an advantageous embodiment. Road detector1800 may be implemented in context recognition module 234 in FIG. 2. Inparticular, road detector 1800 is configured to identify roads that maybe present in image 228 in FIG. 2. Roads that are identified may form aportion of group of objects 236 in FIG. 2.

As depicted, road detector 1800 includes filter 1802, registrationmodule 1803, line detector 1804, morphological operations module 1806,and Hough transform 1826. Filter 1802 is configured to reduce noise inthe form of speckle noise from image 228 to form processed image 1810.Of course, filter 1802 may be configured to reduce other types of noisein addition to and/or in place of speckle noise.

Registration module 1803 is configured to receive processed image 1810.Registration module 1803 registers number of previous images 1811 withprocessed image 1810 to form registered images 1812. Number of previousimages 1811 includes one or more previously filtered synthetic apertureradar images. Registered images 1812 are number of previous images 1811aligned with and overlaid over processed image 1810.

Further, registration module 1803 performs merging operations 1814 toform merged image 1816 using registered images 1812. Merging operations1814 include selecting the maximum value of corresponding pixels inregistered images 1812 as the value for the corresponding pixels inmerged image 1816.

Shadows may have a level of intensity similar to roads. However, shadowscast by objects, such as buildings, structures, vegetation, and/or othertypes of objects, may be in different locations and/or have differentsizes in registered images when the images are generated from differentviewpoints and/or by different camera systems. As a result, a pixel fora shadow cast by an object, such as a building, in processed image 1810may correspond to a pixel in another image in registered images 1812that is not for the same shadow.

Further, lines that may be identified for roads may be in substantiallythe same locations in registered images. In this manner, a pixel for aroad in processed image 1810 may correspond to a pixel for the same roadin the other images in registered images 1812.

In this manner, merging operations 1814 reduces the presence ofundesired shadows and/or other features that may cause a falseidentification of roads.

Merged image 1816 is sent to both line detector 1804 and morphologicaloperations module 1806. Line detector 1804 is configured to detect lines1818. Line detector 1804 may be implemented using the algorithmdeveloped by Carsten Steger, which is similar to line detector 304 inFIG. 3. However, in these illustrative examples, lines 1818 are darklines. In this illustrative example, line detector 1804 generates binaryimage 1819. In binary image 1819, pixels corresponding to lines 1818have a value of “1”.

Morphological operations module 1806 applies selected threshold 1820 tomerged image 1816 to generate binary image 1822. In this illustrativeexample, selected threshold 1820 may be a low level of intensity for thepixels in merged image 1816. In particular, a pixel in binary image 1822has a value of “1” when the corresponding pixel in merged image 1816 hasa value less than selected threshold 1820.

Additionally, morphological operations module 1806 is configured toprocess binary image 1822 using morphological image processing to formbinary mask 1824. Morphological image processing may includemorphological operations, such as, for example, without limitation, blobanalysis, erosion, dilation, opening, closing, and/or other suitabletypes of morphological operations. Binary mask 1824 may be white areasin which pixels have a value of “1”. These white areas are areas thatmay potentially be roads.

Road mask generation module 1808 is configured to apply binary mask 1824to binary image 1819 to identify which of lines 1818 are in the whiteareas of binary mask 1824. Road mask generation module 1808 uses Houghtransform 1826 to identify which of the lines in the white areas ofbinary mask 1824 may be for roads. In particular, Hough transform 1826is used to identify set of roads 1828 based on which of the lines in thewire areas of binary mask 1824 are substantially straight lines forroads. Based on the results of Hough transform 1826, road maskgeneration module 1808 generates road mask 1830 identifying set of roads1828.

With reference now to FIG. 19, an illustration of an image generatedusing a synthetic aperture radar imaging system is depicted inaccordance with an advantageous embodiment. In this illustrativeexample, image 1900 may be generated using a synthetic aperture radarimaging system, such as synthetic aperture radar imaging system 212 inFIG. 2. As depicted, road 1902 is present in image 1900.

Turning now to FIG. 20, an illustration of a processed image for use inidentifying roads is depicted in accordance with an advantageousembodiment. In this illustrative example, binary image 2000 may begenerated using a road detector, such as road detector 1800 in FIG. 18.In particular, binary image 2000 is an example of one implementation forbinary image 1819 in FIG. 18.

With reference now to FIG. 21, an illustration of a road mask for animage is depicted in accordance with an advantageous embodiment. In thisillustrative example, road mask 2100 may be generated by a roaddetector, such as road detector 1800 in FIG. 18. Road mask 2100 isoverlaid on image 1900 from FIG. 19.

With reference now to FIG. 22, an illustration of a flowchart of aprocess for identifying a target object is depicted in accordance withan advantageous embodiment. The process illustrated in FIG. 22 may beimplemented using image processing module 210 in FIG. 2.

The process begins by receiving an image (operation 2200). The imagereceived may have been generated using, for example, synthetic apertureradar imaging system 212 in FIG. 2. The process then preprocesses theimage to reduce noise in the image (operation 2202). In operation 2202,the image may be preprocessed to reduce noise in the form of specklenoise in this illustrative example.

Next, the process identifies a group of objects in the image (operation2204). The group of objects provides a context for identifying thetarget object. The group of objects may include, for example, buildings,roads, vegetation, and/or other objects. The process then identifies anumber of regions in the image to search for the target object based onthe context from the group of objects (operation 2206). Thereafter, theprocess searches for the target object in the image using the contextprovided by the group of objects (operation 2208), with the processterminating thereafter.

With reference now to FIG. 23, an illustration of a process foridentifying a target object in an image is depicted in accordance withan advantageous embodiment. The process illustrated in FIG. 23 may beimplemented using image processing module 210 in FIG. 2. Further, thisprocess may be a more-detailed process of the process illustrated inFIG. 22.

The process begins by receiving an image generated using a syntheticaperture radar imaging system (operation 2300). The process thenpreprocesses the image to reduce noise in the image (operation 2302).Next, the process generates a building mask for the image (operation2304). The process then generates a vegetation mask for the image(operation 2306). Then, the process generates a road mask for the image(operation 2308).

Thereafter, the process identifies contextual information for the imageusing the building mask, the vegetation mask, the road mask, and/oradditional information (operation 2310). The additional information mayinclude, for example, weather data, traffic data, geographicalinformation, maps, satellite images, mission details, and/or othersuitable types of information.

Next, the process uses the contextual information for the image toidentify a number of regions in the image within which to search for atarget object (operation 2312). A region in the number of regions maybe, for example, within a selected distance from one or more of thebuilding mask, the vegetation mask, and the road mask. In someillustrative examples, a region may be within the road mask when thetarget object is a vehicle traveling on a road.

The process then searches for the target object in the number of regionsusing a number of agents and a particle-swarm optimization process(operation 2314), with the process terminating thereafter. In operation2314, each agent in the number of agents searches for the target objectwithin a window for the agent.

With reference now to FIG. 24, an illustration of a process fordetecting buildings in an image is depicted in accordance with anadvantageous embodiment. The process illustrated in FIG. 24 may beimplemented using, for example, building detector 300 in FIG. 3.Further, this process may be used to implement operation 2304 in FIG.23.

The process begins by identifying a set of lines in an image (operation2400). In operation 2400, the image is a processed image. In particular,the processed image has been processed to substantially remove noise,such as speckle noise, from the image.

The process then identifies a set of bright areas in the image(operation 2402). Next, the process identifies a set of shadows in theimage (operation 2404). Thereafter, the process identifies a set ofbuildings in the image using the set of lines, the set of bright areas,and the set of shadows (operation 2406). The process generates abuilding mask for the image that identifies the set of buildings andlocations for the set of buildings in the image (operation 2408), withthe process terminating thereafter.

With reference now to FIG. 25, an illustration of a flowchart of aprocess for detecting vegetation in an image is depicted in accordancewith an advantageous embodiment. The process illustrated in FIG. 25 maybe implemented using, for example, vegetation detector 1400 in FIG. 14.Further, this process may be used to implement operation 2306 in FIG.23.

The process begins by applying a texture features filter to an image toidentify texture features (operation 2500). The process then generates atexton map using the texture features identified and a texton codebook(operation 2502). Next, the process forms a number of texton channelsusing the texton map (operation 2504).

The process applies a number of texton layout filters to the image usingthe number of texton channels to identify a number of feature vectors(operation 2506). Thereafter, the process identifies a texture maskusing the number of feature vectors and a number of boosted classifiers(operation 2508), with the process terminating thereafter.

With reference now to FIG. 26, an illustration of a flowchart of aprocess for detecting roads in an image is depicted in accordance withan advantageous embodiment. The process illustrated in FIG. 26 may beimplemented using, for example, road detector 1800 in FIG. 18.

The process begins by applying a filter to an image to reduce noise inthe image and form a processed image (operation 2600). The noise may bein the form of, for example, speckle noise. The process registers anumber of previously processed images with the processed image to formregistered images (operation 2602). The process generates a merged imageusing the registered images (operation 2604).

Thereafter, the process generates a binary image identifying lines usingthe merged image (operation 2606). These lines represent the dark linesfrom the processed image. The process generates a binary mask using themerged image and a number of morphological operations (operation 2608).Next, the process applies the binary mask to the binary image toidentify lines from the set of lines that are in white areas in thebinary mask (operation 2610).

The process then uses a Hough transform to identify which of the linesfrom the set of lines that are in the white areas in the binary mask arefor roads (operation 2612). Thereafter, the process generates a roadmask identifying the lines that are for roads (operation 2614), with theprocess terminating thereafter.

The flowcharts and block diagrams in the different depicted embodimentsillustrate the architecture, functionality, and operation of somepossible implementations of apparatus and methods in an advantageousembodiment. In this regard, each block in the flowcharts or blockdiagrams may represent a module, segment, function, and/or a portion ofan operation or step. For example, one or more of the blocks may beimplemented as program code, in hardware, or a combination of theprogram code and hardware. When implemented in hardware, the hardwaremay, for example, take the form of integrated circuits that aremanufactured or configured to perform one or more operations in theflowcharts or block diagrams.

In some alternative implementations of an advantageous embodiment, thefunction or functions noted in the block may occur out of the ordernoted in the figures. For example, in some cases, two blocks shown insuccession may be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. Also, other blocks may be added in addition tothe illustrated blocks in a flowchart or block diagram.

Turning now to FIG. 27, an illustration of a data processing system isdepicted in accordance with an advantageous embodiment. In thisillustrative example, data processing system 2700 may be used toimplement one or more of number of computers 231 in FIG. 2. Dataprocessing system 2700 includes communications fabric 2702, whichprovides communications between processor unit 2704, memory 2706,persistent storage 2708, communications unit 2710, input/output (I/O)unit 2712, and display 2714.

Processor unit 2704 serves to execute instructions for software that maybe loaded into memory 2706. Processor unit 2704 may be a number ofprocessors, a multi-processor core, or some other type of processor,depending on the particular implementation. A number, as used hereinwith reference to an item, means one or more items. Further, processorunit 2704 may be implemented using a number of heterogeneous processorsystems in which a main processor is present with secondary processorson a single chip. As another illustrative example, processor unit 2704may be a symmetric multi-processor system containing multiple processorsof the same type.

Memory 2706 and persistent storage 2708 are examples of storage devices2716. A storage device is any piece of hardware that is capable ofstoring information, such as, for example, without limitation, data,program code in functional form, and/or other suitable informationeither on a temporary basis and/or a permanent basis. Storage devices2716 may also be referred to as computer readable storage devices inthese examples. Memory 2706, in these examples, may be, for example, arandom access memory or any other suitable volatile or non-volatilestorage device. Persistent storage 2708 may take various forms,depending on the particular implementation.

For example, persistent storage 2708 may contain one or more componentsor devices. For example, persistent storage 2708 may be a hard drive, aflash memory, a rewritable optical disk, a rewritable magnetic tape, orsome combination of the above. The media used by persistent storage 2708also may be removable. For example, a removable hard drive may be usedfor persistent storage 2708.

Communications unit 2710, in these examples, provides for communicationswith other data processing systems or devices. In these examples,communications unit 2710 is a network interface card. Communicationsunit 2710 may provide communications through the use of either or bothphysical and wireless communications links.

Input/output unit 2712 allows for input and output of data with otherdevices that may be connected to data processing system 2700. Forexample, input/output unit 2712 may provide a connection for user inputthrough a keyboard, a mouse, and/or some other suitable input device.Further, input/output unit 2712 may send output to a printer. Display2714 provides a mechanism to display information to a user.

Instructions for the operating system, applications, and/or programs maybe located in storage devices 2716, which are in communication withprocessor unit 2704 through communications fabric 2702. In theseillustrative examples, the instructions are in a functional form onpersistent storage 2708. These instructions may be loaded into memory2706 for execution by processor unit 2704. The processes of thedifferent embodiments may be performed by processor unit 2704 usingcomputer-implemented instructions, which may be located in a memory,such as memory 2706.

These instructions are referred to as program code, computer usableprogram code, or computer readable program code that may be read andexecuted by a processor in processor unit 2704. The program code in thedifferent embodiments may be embodied on different physical or computerreadable storage media, such as memory 2706 or persistent storage 2708.

Program code 2718 is located in a functional form on computer readablemedia 2720 that is selectively removable and may be loaded onto ortransferred to data processing system 2700 for execution by processorunit 2704. Program code 2718 and computer readable media 2720 formcomputer program product 2722 in these examples. In one example,computer readable media 2720 may be computer readable storage media 2724or computer readable signal media 2726. Computer readable storage media2724 may include, for example, an optical or magnetic disk that isinserted or placed into a drive or other device that is part ofpersistent storage 2708 for transfer onto a storage device, such as ahard drive, that is part of persistent storage 2708.

Computer readable storage media 2724 also may take the form of apersistent storage, such as a hard drive, a thumb drive, or a flashmemory, that is connected to data processing system 2700. In someinstances, computer readable storage media 2724 may not be removablefrom data processing system 2700. In these examples, computer readablestorage media 2724 is a physical or tangible storage device used tostore program code 2718 rather than a medium that propagates ortransmits program code 2718. Computer readable storage media 2724 isalso referred to as a computer readable tangible storage device or acomputer readable physical storage device. In other words, computerreadable storage media 2724 is a media that can be touched by a person.

Alternatively, program code 2718 may be transferred to data processingsystem 2700 using computer readable signal media 2726. Computer readablesignal media 2726 may be, for example, a propagated data signalcontaining program code 2718. For example, computer readable signalmedia 2726 may be an electromagnetic signal, an optical signal, and/orany other suitable type of signal. These signals may be transmitted overcommunications links, such as wireless communications links, opticalfiber cable, coaxial cable, a wire, and/or any other suitable type ofcommunications link. In other words, the communications link and/or theconnection may be physical or wireless in the illustrative examples.

In some advantageous embodiments, program code 2718 may be downloadedover a network to persistent storage 2708 from another device or dataprocessing system through computer readable signal media 2726 for usewithin data processing system 2700. For instance, program code stored ina computer readable storage medium in a server data processing systemmay be downloaded over a network from the server to data processingsystem 2700. The data processing system providing program code 2718 maybe a server computer, a client computer, or some other device capable ofstoring and transmitting program code 2718.

The different components illustrated for data processing system 2700 arenot meant to provide architectural limitations to the manner in whichdifferent embodiments may be implemented. The different advantageousembodiments may be implemented in a data processing system includingcomponents in addition to or in place of those illustrated for dataprocessing system 2700. Other components shown in FIG. 27 can be variedfrom the illustrative examples shown. The different embodiments may beimplemented using any hardware device or system capable of runningprogram code. As one example, the data processing system may includeorganic components integrated with inorganic components and/or may becomprised entirely of organic components excluding a human being. Forexample, a storage device may be comprised of an organic semiconductor.

In another illustrative example, processor unit 2704 may take the formof a hardware unit that has circuits that are manufactured or configuredfor a particular use. This type of hardware may perform operationswithout needing program code to be loaded into a memory from a storagedevice to be configured to perform the operations.

For example, when processor unit 2704 takes the form of a hardware unit,processor unit 2704 may be a circuit system, an application specificintegrated circuit (ASIC), a programmable logic device, or some othersuitable type of hardware configured to perform a number of operations.With a programmable logic device, the device is configured to performthe number of operations. The device may be reconfigured at a later timeor may be permanently configured to perform the number of operations.Examples of programmable logic devices include, for example, aprogrammable logic array, a programmable array logic, a fieldprogrammable logic array, a field programmable gate array, and othersuitable hardware devices. With this type of implementation, programcode 2718 may be omitted because the processes for the differentembodiments are implemented in a hardware unit.

In still another illustrative example, processor unit 2704 may beimplemented using a combination of processors found in computers andhardware units. Processor unit 2704 may have a number of hardware unitsand a number of processors that are configured to run program code 2718.With this depicted example, some of the processes may be implemented inthe number of hardware units, while other processes may be implementedin the number of processors.

In another example, a bus system may be used to implement communicationsfabric 2702 and may be comprised of one or more buses, such as a systembus or an input/output bus. Of course, the bus system may be implementedusing any suitable type of architecture that provides for a transfer ofdata between different components or devices attached to the bus system.

Additionally, a communications unit may include a number of devices thattransmit data, receive data, or transmit and receive data. Acommunications unit may be, for example, a modem or a network adapter,two network adapters, or some combination thereof. Further, a memory maybe, for example, memory 2706, or a cache, such as found in an interfaceand memory controller hub that may be present in communications fabric2702.

Thus, the different advantageous embodiments provide a method andapparatus for identifying a target object in an image. In particular, amethod and apparatus for identifying a target object in an image usingcontextual information identified in the image is provided. In oneadvantageous embodiment, a group of objects are identified in an image.The group of objects provides a context for identifying a target objectin the image. The image is then searched for the target object using thecontext provided by the group of objects.

In this manner, the different advantageous embodiments provide a systemfor identifying target objects more rapidly and/or accurately thancurrently-available systems. Additionally, the amount of computingresources needed to identify target objects in images may be reduced.Further, the time and/or effort spent by an operator identifying whichidentifications of target objects are false identifications may bereduced.

The description of the different advantageous embodiments has beenpresented for purposes of illustration and description and is notintended to be exhaustive or limited to the embodiments in the formdisclosed. Many modifications and variations will be apparent to thoseof ordinary skill in the art.

Further, different advantageous embodiments may provide differentadvantages as compared to other advantageous embodiments. The embodimentor embodiments selected are chosen and described in order to bestexplain the principles of the embodiments, the practical application,and to enable others of ordinary skill in the art to understand thedisclosure for various embodiments with various modifications as aresuited to the particular use contemplated.

1. A method for identifying a target object, the method comprising:identifying a group of objects in an image, wherein the group of objectsprovides a context for identifying the target object in the image; andsearching for the target object in the image using the context providedby the group of objects.
 2. The method of claim 1, wherein searching forthe target object in the image using the context provided by the groupof objects comprises: identifying a number of regions in the image tosearch for the target object based on the context provided by the groupof objects.
 3. The method of claim 2, wherein a region in the number ofregions is selected from one of within the group of objects and within aselected distance from the group of objects.
 4. The method of claim 1,wherein searching for the target object in the image using the contextprovided by the group of objects comprises: responsive to identifying aparticular object in the image as the target object, determining whetherthe particular object is the target object based on a location of theparticular object to the group of objects.
 5. The method of claim 1,wherein searching for the target object in the image using the contextprovided by the group of objects comprises: searching for the targetobject in the image using the context provided by the group of objectswith a number of agents, wherein an agent in the number of agents isconfigured to search for the target object within a window for theagent.
 6. The method of claim 1, wherein searching for the target objectin the image using the context provided by the group of objectscomprises: searching for the target object in the image in a number ofregions in the image using a particle-swarm optimization process,wherein the number of regions is identified using the context providedby the group of objects.
 7. The method of claim 1, wherein identifyingthe group of objects in the image, wherein the group of objects providesthe context for identifying the target object in the image comprises:identifying the group of objects in the image; forming a number of masksfor the group of objects for use in searching for the target object inthe image; and using the number of masks to identify a number of regionsin the image to search for the target object.
 8. The method of claim 1,wherein an object in the group of objects is selected from at least oneof a building, a road, and vegetation.
 9. The method of claim 1, whereinidentifying the group of objects in the image, wherein the group ofobjects provides the context for identifying the target object in theimage comprises: identifying a set of lines in the image; identifying aset of bright areas in the image; identifying a set of shadows in theimage; and identifying a set of buildings in the image using at leastone of the set of lines, the set of bright areas, and the set ofshadows.
 10. The method of claim 1, wherein identifying the group ofobjects in the image, wherein the group of objects provides the contextfor identifying the target object in the image comprises: identifyingtexture features for the image; identifying a texton map using thetexture features and a texton codebook; applying a number of textonlayout filters to the image using a number of texton channels formedusing the texton map to identify a number of feature vectors; andidentifying a texture mask using the number of feature vectors and anumber of boosted classifiers.
 11. The method of claim 1, whereinidentifying the group of objects in the image, wherein the group ofobjects provides the context for identifying the target object in theimage comprises: reducing noise in the image to form a processed image;registering a number of previous images with the processed image to formregistered images; performing a number of merging operations to form amerged image using the registered images; identifying a set of lines forroads using the merged image, a line detector, a number of morphologicaloperations, and a Hough transform; and generating a road maskidentifying a set of roads using the set of lines identified.
 12. Themethod of claim 1 further comprising: receiving the image from asynthetic aperture radar imaging system; and processing the image toreduce noise present in the image prior to identifying the group ofobjects in the image.
 13. An apparatus comprising: a context recognitionmodule configured to identify a group of objects in an image, whereinthe group of objects provides a context for identifying a target objectin the image; and a target recognition module configured to search forthe target object in the image using the context provided by the groupof objects.
 14. The apparatus of claim 13, wherein the contextrecognition module comprises: a building detector configured to identifya set of buildings in the image and generate a building mask for use insearching for the target object in the image.
 15. The apparatus of claim13, wherein the context recognition module comprises: a vegetationdetector configured to identify vegetation in the image and generate avegetation mask for use in searching for the target object in the image.16. The apparatus of claim 15, wherein the context recognition modulecomprises: a road detector configured to identify a set of roads in theimage and generate a road mask for use in searching for the targetobject in the image.
 17. The apparatus of claim 13, wherein the contextrecognition module is configured to identify a number of regions in theimage to search for the target object based on the context provided bythe group of objects.
 18. The apparatus of claim 17, wherein the targetrecognition module is configured to search for the target object in thenumber of regions in the image using a particle-swarm optimizationprocess.
 19. The apparatus of claim 13, wherein the image is generatedusing a synthetic aperture radar imaging system.
 20. The apparatus ofclaim 13, wherein the context recognition module comprises: a noisefilter configured to reduce noise present in the image prior to thecontext recognition module identifying the group of objects in theimage.